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Update app.py

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  1. app.py +10 -4
app.py CHANGED
@@ -12,6 +12,8 @@ import requests
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  from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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  from qwen_vl_utils import process_vision_info
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@@ -33,19 +35,23 @@ processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-larg
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  model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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  processor1 = BlipProcessor.from_pretrained("noamrot/FuseCap")
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  model2 = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap")
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- pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium")
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  # model3 =model = Qwen2VLForConditionalGeneration.from_pretrained(
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  # "prithivMLmods/Qwen2-VL-OCR-2B-Instruct", torch_dtype="auto", device_map="auto"
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  # )
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  # processor2 = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-2B-Instruct")
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- pipe.to(device)
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  model2.to(device)
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  model.to(device)
 
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@@ -107,10 +113,10 @@ def generate_caption_and_image(image):
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  prompt = f'''Create a highly realistic clothing item based on the following descriptions: The design should reflect {caption1} and {caption2}, blending both themes into a single, stylish, and modern piece of clothing. Incorporate highly realistic and high-quality textures that exude sophistication, with realistic fabric lighting and fine details. Subtly hint at {selected_fabric}, featuring a {selected_pattern} motif and a {selected_textile_design} style that harmoniously balances the essence of both captions.'''
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-
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  # Generate image based on the caption
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- generated_image = pipe(prompt).images[0]
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  return prompt, generated_image
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  from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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  from qwen_vl_utils import process_vision_info
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+ from diffusers import DiffusionPipeline
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+
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  model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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  processor1 = BlipProcessor.from_pretrained("noamrot/FuseCap")
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  model2 = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap")
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+ # pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium")
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  # model3 =model = Qwen2VLForConditionalGeneration.from_pretrained(
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  # "prithivMLmods/Qwen2-VL-OCR-2B-Instruct", torch_dtype="auto", device_map="auto"
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  # )
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  # processor2 = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-2B-Instruct")
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+ pipe3 = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
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+ pipe3.load_lora_weights("tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator")
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+
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ # pipe.to(device)
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  model2.to(device)
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  model.to(device)
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+ pip3.to(device)
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  prompt = f'''Create a highly realistic clothing item based on the following descriptions: The design should reflect {caption1} and {caption2}, blending both themes into a single, stylish, and modern piece of clothing. Incorporate highly realistic and high-quality textures that exude sophistication, with realistic fabric lighting and fine details. Subtly hint at {selected_fabric}, featuring a {selected_pattern} motif and a {selected_textile_design} style that harmoniously balances the essence of both captions.'''
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+ ]
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  # Generate image based on the caption
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+ generated_image = pipe3(prompt).images[0]
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  return prompt, generated_image
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